일개 종합병원 응급실 환자 중증도 분류도구의 임상 타당성 비교연구: mESI와 mCTAS중심으로
Bibliographic record
Abstract
Purpose: This study was conducted to identify better methods of determining the severity of triage by comparing triage results and clinical outcome of patients categorized by the modified Canadian Triage Acuity Scale (mCTAS) and modified Emergency Severity Index (mESI). Methods: Subjects enrolled in this study consisted of 1,000 adult patients (age 16 years or older) who visited the emergency room of a university affiliated hospital between September 15, 2011 and September 30, 2011 and were categorized into five levels by mCTAS and mESI. Results: 1) Good confidence was verified based on weighted kappa values of 0.70 between the physicians group and nurses group. 2) Upon evaluation of triage by mESI, the majority of patients were at level 3 among 5, followed by level 4, 2, 1 and 5 in order. The same level orders were shown upon evaluation of triage by mCTAS beside differences in patient numbers. 3) Comparing clinical outcome according to the mCTAS and the mESI revealed similar results in both triage tools, with a higher triage level being associated with a higher admission rate and lower triage level and the discharge rate became higher. Conclusion: Triage by mESI showed good agreement among asserters and high agreement between physicians and nurses. Clinical results based on mCTAS and mESI triage showed similar rates of admission to the ward or intensive care unit and rates of discharge. Although these two triage protocols are similar in many aspects, the use of mESI is perceived as a better because mCTAS requires knowledge of various diseases and mESI has a short training period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".